{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ed51720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 574255 entries, 0 to 574254\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   step            574255 non-null  int64  \n",
      " 1   type            574255 non-null  object \n",
      " 2   amount          574255 non-null  float64\n",
      " 3   nameOrig        574255 non-null  object \n",
      " 4   oldbalanceOrg   574255 non-null  float64\n",
      " 5   newbalanceOrig  574255 non-null  float64\n",
      " 6   nameDest        574255 non-null  object \n",
      " 7   oldbalanceDest  574255 non-null  float64\n",
      " 8   newbalanceDest  574255 non-null  float64\n",
      " 9   isFraud         574255 non-null  int64  \n",
      "dtypes: float64(5), int64(2), object(3)\n",
      "memory usage: 43.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# 载入必要库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline \n",
    "import seaborn as sns\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif']='SimHei'\n",
    "\n",
    "# 忽略所有警告\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 1 导入数据\n",
    "data = pd.read_csv('./金融反欺诈数据集.csv')\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc2c2c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>574255.000000</td>\n",
       "      <td>574255</td>\n",
       "      <td>5.742550e+05</td>\n",
       "      <td>574255</td>\n",
       "      <td>5.742550e+05</td>\n",
       "      <td>5.742550e+05</td>\n",
       "      <td>574255</td>\n",
       "      <td>5.742550e+05</td>\n",
       "      <td>5.742550e+05</td>\n",
       "      <td>574255.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>574184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>249521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C1842781381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C985934102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>204397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.826483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.604372e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.907349e+05</td>\n",
       "      <td>9.103838e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.723953e+05</td>\n",
       "      <td>1.138243e+06</td>\n",
       "      <td>0.000472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.242076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.660727e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.972398e+06</td>\n",
       "      <td>3.009468e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.316947e+06</td>\n",
       "      <td>2.479018e+06</td>\n",
       "      <td>0.021719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.237152e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.541418e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.796400e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.119652e+05</td>\n",
       "      <td>2.054445e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.147138e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.584455e+05</td>\n",
       "      <td>1.966670e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.868961e+05</td>\n",
       "      <td>1.169781e+06</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.890000e+07</td>\n",
       "      <td>3.890000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.150000e+07</td>\n",
       "      <td>4.150000e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 step      type        amount     nameOrig  oldbalanceOrg  \\\n",
       "count   574255.000000    574255  5.742550e+05       574255   5.742550e+05   \n",
       "unique            NaN         5           NaN       574184            NaN   \n",
       "top               NaN  CASH_OUT           NaN  C1842781381            NaN   \n",
       "freq              NaN    204397           NaN            2            NaN   \n",
       "mean        14.826483       NaN  1.604372e+05          NaN   8.907349e+05   \n",
       "std          4.242076       NaN  2.660727e+05          NaN   2.972398e+06   \n",
       "min          1.000000       NaN  1.000000e-01          NaN   0.000000e+00   \n",
       "25%         11.000000       NaN  1.237152e+04          NaN   0.000000e+00   \n",
       "50%         15.000000       NaN  7.541418e+04          NaN   1.796400e+04   \n",
       "75%         18.000000       NaN  2.147138e+05          NaN   1.584455e+05   \n",
       "max         24.000000       NaN  1.000000e+07          NaN   3.890000e+07   \n",
       "\n",
       "        newbalanceOrig    nameDest  oldbalanceDest  newbalanceDest  \\\n",
       "count     5.742550e+05      574255    5.742550e+05    5.742550e+05   \n",
       "unique             NaN      249521             NaN             NaN   \n",
       "top                NaN  C985934102             NaN             NaN   \n",
       "freq               NaN          93             NaN             NaN   \n",
       "mean      9.103838e+05         NaN    9.723953e+05    1.138243e+06   \n",
       "std       3.009468e+06         NaN    2.316947e+06    2.479018e+06   \n",
       "min       0.000000e+00         NaN    0.000000e+00    0.000000e+00   \n",
       "25%       0.000000e+00         NaN    0.000000e+00    0.000000e+00   \n",
       "50%       0.000000e+00         NaN    1.119652e+05    2.054445e+05   \n",
       "75%       1.966670e+05         NaN    8.868961e+05    1.169781e+06   \n",
       "max       3.890000e+07         NaN    4.150000e+07    4.150000e+07   \n",
       "\n",
       "              isFraud  \n",
       "count   574255.000000  \n",
       "unique            NaN  \n",
       "top               NaN  \n",
       "freq              NaN  \n",
       "mean         0.000472  \n",
       "std          0.021719  \n",
       "min          0.000000  \n",
       "25%          0.000000  \n",
       "50%          0.000000  \n",
       "75%          0.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数值和离散型数据的基本统计信息 \n",
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc56c688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, ..., 24, 24, 24], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看'step'这一列有哪些值，并且按照从小到大的顺序排列\n",
    "np.sort(data['step'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdbbbffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '交易笔数')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CC+f60HYPdXyN5X22PYSrdfyG3V+vHQljRvCa/4dgxvcCd38tYfwDBOsMH0fwvXymA+U/SNAmkiiX3f9vfYWglWPOPlpS4n8HjiP4IaSWVjvfufsCM7uG4Gv1sJkdF87UfxOoDsPyJIKe4BOAS/29G6VUEexQd0b48X8By9xdIVgkySkIi0iyyidYLisehOOzmfEQczBB2IRgR7ePEYTgPIKZ42+TsGqCu88EMLNFBJtePEnQr/o68Gt3b1mv1syqCXqO4wHrJuBR4MXE5b5aC9shHgUeDcPhJGCPnd7CGq8Lz70oXG4s0XyC4P4w8L/u3pEd6Nryb4ILx5YQ/Cq/dbtGUfjnPoOwB9sjTyWYXb6X964NPJDg670TOMdbbZVMcFHi3QStJp/ycO3lfTzfIvbcMKRXeMPd5+3rMULxIFxB8HX9sbvv0aPs7n8yswaCC/wqw2N/ATCzcQR/T2oJ2mta9x7/nWCFij8S/LZhDfu+cFJEkoDteUGviEj6MrPsvfTv9mQdbW5BnMzM7CCCzT3aWrd3CLDD3dvdijmVmdk5wCveagc7EUltCsIiIiIikpG0fJqIiIiIZCQFYRERERHJSArCIiIiIpKRtGrEPoRXfg8lWBJHRERERJJTMbChMxciKwjv21D2XMxeRERERJLPcPZcM71dCsL7VgWwdu1aSkpK9nWuiIiIiPSwyspKRowYAZ38Db6CcAeVlJQoCIuIiIikEV0sJyIiIiIZSUFYRERERDKSgrCIiIiIZCQFYRERERHJSArCIiIiIpKRFIRFREREJCMpCIuIiIhIRlIQFhEREZGMpCAsIiIiIhlJQVhEREREMpKCsIiIiIhkJAVhEREREclICsIiIiIikpEUhEVEREQkIykIi4iIiEhGUhAWERERkYykICwiIiLdbs22Gj5375t87LZXqKhpjLocEQByoi5ARERE0ldNQxO/fW45t89aQUNTDIBHF2zkomNHRlyZiGaERUREpBu4O4/M38DpN8/klufepaEpRllRPgAzl26JuDqRgGaERUREpEst3FDJ9x95h9krtwMwvG8vvnvOJIaUFnDeb17ipXe30dgcIzdb83ESLQVhERER6RI7axq4+aml3PPaamIOBblZfPaUg7jm5LEU5GYTizn9euexvbqBN1fv4Nix/aMuWTKcgrCIiIgckOaYc9/sNdz01BJ2hhfCnXPYEK7/wCEM69Or5bysLOPk8WX8a94Gnl+6VUFYIqffSYiIiMh+m71yOx/89Yt8919vs7OmkQmDirn36mP5zUVHvCcEx82YMACAmUu29nSpInvQjLCIiIh02saKWn762GIenr8BgJKCHL565gQuPnYkOXvp/T1pfBCEF26sZEtlHQNLCnqkXpG2RDYjbGa/MjNPuL0bHp9sZnPMbIeZ3WhmlnCfGWa2yMzKzey6Vo/3UTNbbWYbzOwTrcY+Z2abzWyFmZ3WM69QREQk/dQ1NvOb597ltJtm8vD8DZjBRceO5Pmvn8plx4/eawgGKCvKZ8qwUgBeWFbeEyWLtCvK1oijgHOAvuFtmpnlA48Ab4Tjk4DLAcxsAPAwcB8wHbjYzE4NxyYD9wA/BM4CbjCzCeHYWcBNwDXAJcAdZqamJBERkU5wd55euJmzfvECNz65hNrGZo4c1ZdHPn8iPzl/Cv1653X4sU6Jt0csVXuERCuSIGxmOcChwAvuvjO8VQFnA6XAde6+HLgeuDK828XABuCH7r4MuCFh7CrgOXe/w90XALcAl4Zj1wJ3uftD7v4y8BBwfve/ShERkfSwfOsuLr9zDlf9+XVWb6thYHE+v/j44TzwmelMDmd3O2PGwUEQnrVsK80x7+pyRTosqhnhKeFzzzOzWjN7wsxGAlOBV929JjzvLYJZYcKx59w9/o6ZDRyZMPZswuN3dGwPZpZvZiXxG1C8X69QREQkxVXVNfKTxxZx1s9fYObSreRmG9eeMo5nv3YKH542jITuxU45fEQfigty2FnTyFvrdnZt0SKdEFUQngQsIZi1PQxoAm4HSoCV8ZPC0NtsZn1bjwGVwNDw4/0da8u3gYqE27pOvC4REZGUF4s5/3hjHafdPJPbX1hBU8w5feJAnvrKDL75/okU5R/YtfY52VmcNL4MgOe1eoREKJIg7O73uPtR7v5K2ObwWeB9YT31rU6vAwoJwnJ9G8c5gLG2/JSgPSN+G97BlyUiIpLy3lq3k/+69WW++vf5bK2qZ0xZb+68/Gj+cPnRjCnr3WXPE2+PUJ+wRClZlk/bQhCCNwGTW40VAw3AdmBAG8c5gLE9uHs9CcF5f3/tIyIikkrqm5r5/sPvcP+ctbhD77xsvnD6eK44YTT5Odld/nwnh0F4/rqd7KhuoG8nLrYT6SpRXSx3o5ldlHBoOhADFoQfx88bA+QThNk5iWPANGB9+PH+jomIiAjwhxdXct/sIAR/ZNownv3aKXxmxrhuCcEAQ0p7MWFQMe4w610toybRiKpHeD7wIzM73czOBG4F/gw8BZSY2RXhedcDT7t7M8HSaSeY2Rlmlgt8A3gyPO8fwIVmNsXMioAvJow9AHzWzIaZ2SCClSbiYyIiIhmvpqGJO2YFl9P85Pwp/N/HD2dQD2x0cYp2mZOIRdUj/BfgrwQB9j7gCeDz7t5EsBTaLWZWDpwHfDO8TznwFeAxYDMwAfhRODYf+CXwOsFsbzPw2/DpHgGeAZYRXDQ3F/hnt79IERGRFHHf7LVsr25gZL9CPnZUz10ak9gnHNMyahKByHqE3f3bBCs0tD7+sJmNI1ji7FV335YwdquZPQlMBGa5+66Ese+Y2T3AMGCmuzeExx241Mx+BfQOx/RuExERIdgp7raZywH47Cnj9rkzXFc6cnRfCvOyKd9Vz6JNlRw6tPNrEosciCh3lmuXu29y90cTQ3DC2Ep3fzwxBCeMLXT3/8RDcKuxOe7+vEKwiIjIbn9/Yx1bquoZWlrAR47o2YWS8nOyOX5csNmrVo+QKCRlEBYREZHu19AU49bng9ngz5wyjrycno8F8fYIrScsUVAQFhERyVD/mrue9TtrGVCcz8eOGhFJDTMOHgjAm6t3UFnXGEkNkrkUhEVERDJQU3OM3zz/LgDXnDSWgtzuWSZtX0b2L2RMWW+aYs7L7+7RESnSrRSERUREMtC/39rI6m019C3M5eLjRkZai3aZk6goCIuIiGSYWMy55blgNviqk8ZSmBftRrMzwvWEX1i6FV3TLj1JQVhERCTDPPHOJt7dsouSghw+OX1U1OVw3Jj+5OVksX5nLcu37rEolEi3URAWERHJIO7Or58NZoMvP2EMxQW5EVcEvfKyOXZMP0CrR0jPUhAWERHJIM8s2sKijZX0zsvmUyeMjrqcFuoTligoCIuIiGSIYDZ4GQCXTh9Nn8K8iCva7ZSwT/i1FdupaWiKuBrJFArCIiIiGWLWsnLmr6ugIDeLq04aE3U57zFuQBHD+vSioTnGayu2R12OZAgFYRERkQxxS9gbfNExoygryo+4mvcys5bVI9QeIT1FQVhERCQDvLpiG7NXbScvO4tPzxgbdTltUp+w9DQFYRERkQwQnw3+2NHDGVRSEHE1bTt+XH9ysoyV5dWs3lYddTmSARSERURE0tyba3bw4rvl5GQZn5kxLupy2lVckMuRo/oCweYaIt1NQVhERCTNxWeDP3LEMIb3LYy4mr2L9wlrPWHpCQrCIiIiaezt9RU8u3gLWQafPeWgqMvZp3if8MvLt1Hf1BxxNZLuFIRFRETSWHw2+NypQxld1jviavZt0pASBhTnU9vYzOurdkRdjqQ5BWEREZE0tWRTFU+8swkz+NypyT8bDOEyalo9QnqIgrCIiEia+s1zwWzw2ZMHM35QccTVdFxLEFafsHQzBWEREZE0tGLrLv791gYgdWaD4048qIwsgyWbq9hYURt1OZLGFIRFRETS0G+fX07M4YxDBnLo0NKoy+mUvr3zmDqiD6Bl1KR7KQiLiIikmbXba3hw7nog9WaD49QnLD1BQVhERCTN/G7mcppjzknjy5g2sm/U5eyXeBCetaycpuZYxNVIulIQFhERSSMbK2p54PV1AHzhtPERV7P/Dhveh76FuVTVNTF37c6oy5E0pSAsIiKSRm6buYKG5hjHjOnHMWP6RV3OfsvOMk4ar9UjpHspCIuIiKSJrVX13Dd7DQBfTOHZ4Dj1CUt3UxAWERFJE3fMWkF9U4zDR/ThhIP6R13OATvp4DIAFqyvoHxXfcTVSDpSEBYREUkDO6obuPvV1QB88fSDMLOIKzpwA4sLOHRoCQCzlmlWWLqegrCIiEga+ONLK6lpaObQoSWcOmFg1OV0Ge0yJ91JQVhERCTFVdQ28qeXVgHwhdPSYzY4Lh6EX1hWTizmEVcj6UZBWEREJMX9+eVVVNU3cfCgIs6cNDjqcrrUEaP6Upyfw/bqBhasr4i6HEkzCsIiIiIprLq+iT+8tBIIdpHLykqf2WCA3OwsTjgouGhOq0dIV1MQFhERSWF/eXU1O2saGVPWmw8eNjTqcrrFjAlaRk26h4KwiIhIiqprbOb3s1YAcO0p48hOs9nguJPDPuG5a3ZQUdMYcTWSThSERUQk7VXVNbJi666oy+hy989eQ/muBob16cX504ZFXU63GdanF+MHFhFzePHd8qjLkTSiICwiImlt7fYa3v+LWZx280y+99Db1DU2R11Sl6hvaubWmbtng3Oz0/u/9N27zG2JuBJJJ+n9rhERkYy2saKWi+54lfU7awG465XVfOjXL/LOhtRffeAfb6xnU2Udg0ryueCo4VGX0+0S+4TdtYyadA0FYRERSUtbquq4+PevsXZ7LSP7FfLzj09lQHE+y7bs4sO/eYnbX1iesuvSNjbH+O3z7wLw6ZPHkZ+THXFF3e/o0f3olZvN5sp6Fm+qirocSRMKwiIikna27arn4t+/xoryaob16cW9Vx/L+dOG88SXTuJ9kwbR2Oz85LHFXPKH19hYURt1uZ320LwNrNtRS1lRHp84ZmTU5fSIgtxspo/rD2j1COk6CsIiIpJWKmoaufQPs1m2ZReDSvK59+pjGd63EID+RfncfumR/OT8KfTKzebl5dt4/y9m8diCjRFX3XHNMee3zwWzwVedNJZeeek/Gxyn7ZalqykIi4hI2qiqa+STf3yNhRsrKSvK596rj2NU/97vOcfMuOjYkTz6xRM5bHgpFbWNfPaeN/na3+ezq74poso77tEFG1lRXk2fwlwuOW5U1OX0qHgQfn319pT4XknyUxAWEZG0UF3fxBV3zmH+ugr6FuZyz1XHMm5AUbvnjx1QxD+uPZ7PnToOM3jgjXV84JezeGP1jh6sunNiMeeWZ5cB8KkTxlCUnxNxRT1rdFlvRvUvpLHZeWX5tqjLkTSgICwiIimvtqGZK++aw+urd1BSkMPdVx7LhMHF+7xfbnYWXz9rIn+9ZjrD+vRizfYaPnbbK/z8P0tpao71QOWd89TCzSzdvIvi/BwuO3501OVEQsuoSVdSEBYRkZRW39TMNXe/zqsrtlOUn8NdnzqGycNKO/UYx4zpx+NfPokPHz6U5pjzy2eWccFtr7B6W3U3Vd157s6vw9ngy44fTWmv3IgrikY8CD+/RMuoyYFTEBYRkZTV0BTjc/e8yaxl5fTKzebOK45m2si++/VYJQW5/OLCafzywsMpLshh7pqdfOCXs/jb62uTInA9v2Qr72yopDAvm0+dOCbqciIzfVx/8rKzWLejlhXlyfODiqQmBWEREUlJTc0xvvzXuTy9aAv5OVn84bKjOHp0vwN+3PMOH8bjXzqJY8b0o7qhmW888BafvedNdlQ3dEHVnbNhZy1/eXU1V/5pDtfe8wYAlxw3in6983q8lmRRmJfDMWOC77NWj5ADlVld9iIikhaaY87X/j6fxxZsIi87i9suPZLjDyrrsscf3reQ+64+jtteWM7/PbWUx9/exJtrdnDzBYdz4viue57WmmPO3DU7eHbxFp5dvGWPjSMmDi7mmpPHdtvzp4oZBw/gxXfLmbl0a0bPjsuBs2T4dU8yM7MSoKKiooKSkpKoyxERyXixmPPtfy7gr6+vJSfL+O3FR3DmoYO77fkWrKvgS3+dy4qtwa/hrzpxDF9//4Qu282toqaRmcu28uyizcxcupUdNY0tY1kG00b25bSJAzlt4kAmDi7GzLrkeVPZ0s1VnPnzF8jPyWL+986kIDdz1lKWtlVWVlJaWgpQ6u6VHb2fgvA+KAiLiCQPd+e/H3qHu19dTZbBrz9xBOccNqTbn7emoYkfP7qIe15bAwQzs7/6xDQOHrTvlSlac3eWbdnVMuv7xuodNCds9VxSkMOMCQM5beIAZhw8MKPbINrj7hz/P8+ysaKOuz51TMsFdJK59jcIqzVCRERSgrvzo0cXcferqzGDmz82tUdCMAR9qT8+fwqnThjIN/7xFos3VfGhX7/It8+eyGXHj97nLG1dYzOvrNjGc2H4Xbfjvds6HzyoiFMnDuS0CQM5clRfcrJ1Cc/emBkzDh7A/XPWMnPJVgVh2W8KwiIikhJuemoJf3hxJQD/85EpnD9teI/XcMakQTwx4iS+/ve3mLl0K99/ZCHPLdnKjRccxsDigvecu7GilucWb+XZxZt56d1t1DY2t4zl5WQxfWx/Tj9kIKdOGMiIfoU9/VJSXksQXroFmBR1OZKi1BqxD2qNEBGJ3q+fWcbN/1kKwA3nHconp4+OtB5358+vrOYnjy2ivilGv955/PQjUygryue5xVt4ZvEWFm18729nB5cUBLO+EwdywkH9KczTXNSBqKht5Igf/ofmmDPrG6fqh4kMp9YIERFJS7fNXN4Sgr97ziGRh2AIfjV/2fGjmT6uP1+6fx6LNlby6bvfaHUOTBvRh9MmDuTUiQOZNKREF7p1odJeuRw5si+zV21n5tKtXHLcqKhLkhSkICwiIknrTy+t5KePLwbg62dN4KqTkmvpsIMHFfOvzx3PzU8t5Y5ZK+idn8OMgwdw2sSBzDh4AP2L8qMuMa3NmDBAQVgOiFoj9kGtESIi0bhv9hq+/c8FAHzhtIP46pkTIq5o72obmsnJNnJ1oVuPeXt9BR/89Yv0zstm7n+fSV6OvvaZan9bI/Q3RkREks4/3ljH9Q8GIfiak8dy3fsOjriifeuVl60Q3MMmDSmhrCiP6oZm3li9I+pyJAXpHSsiIknlkfkb+PoD83GHy6aP4ttnT1RvrbQpK8s4eXywdNrMpdpuWTpPQVhERJLGk+9s4st/nUfM4RPHjOB7HzpUIVj2asYEBWHZfwrCIiKSFJ5bvIXP3/smzTHnI9OG8eMPTyErSyFY9u6k8QMwg0UbK9lcWRd1OZJiFIRFRCRyL71bzqf/8gaNzc45hw3hZx89TCFYOqRf7zwOG94H0KywdJ6CsIiIROqtdTu56q7XaWiK8b5Jg/jFxw/XFsPSKfEtlhWEpbP0L42IiERmZ00D1/7lTWobmzn54AHcctE0rbwgnRYPwi8uK6epORZxNZJK9K+NiIhEIhZzvvq3+azfWcuo/oXcctE08nOyoy5LUtDU4aWU9sqloraR+esqoi5HUoiCsIiIROK2F1bwzOIt5OVk8duLj6CkIDfqkiRF5WRnceL4MkDtEdI5CsIiItLjXluxjZueWgLAD849lEOHlkZckaQ69QnL/lAQFhGRHrW1qp4v3De3ZZm0C48eEXVJkgbiQfitdTvZXt0QcTWSKhSERUSkxzTHnC/dP5ctVfWMH1jEj86frA0zpEsMKingkCEluMOsZZoVlo5REBYRkR7zy6eX8vLybRTmZfO7S46gMC8n6pIkjbS0RyxREJaOURAWEZEeMXPpVn793LsA/PQjUzhoYHHEFUm6OXZMPwDe2VAZcSWSKpIiCJvZE2Z2efjxDDNbZGblZnZdq/M+amarzWyDmX2i1djnzGyzma0ws9Najf3YzHaY2Vtmdli3vyAREXmPDTtr+fL9c3GHi48dyXmHD4u6JElDY8p6A7B6ezWxmEdcjaSCyIOwmV0MnBV+PAB4GLgPmA5cbGanhmOTgXuAH4bn32BmE8Kxs4CbgGuAS4A7zKx/OPZp4NPAucB3gfvNLK/HXqCISIZrbI7x+XvfZEdNI5OHlfD/Pjgp6pIkTQ3r24vsLKOuMcaWqvqoy5EUEGkQNrN+wM3AkvDQxcAG4Ifuvgy4AbgyHLsKeM7d73D3BcAtwKXh2LXAXe7+kLu/DDwEnJ8wdpO7z3L3h8PnOrmbX5qIiIT+5/HFvLlmJ8UFOfz2oiMpyNWmGdI9crOzGN63FwCrtlVHXI2kgqhnhG8GHgReDT+fShB247/PmA0cmTD2bMJ99zlmwaXIU/Zyvz2YWb6ZlcRvgJrYRET20xNvb+QPL64E4OYLpjKyf2HEFUm6G9U/bI9QEJYOiCwIhy0PpwPfSDhcAqxM+LwSGHoAY0UEr7G9+7Xl20BFwm3dvl+NiIi0tqq8mq///S0Arjl5LGceOjjiiiQTjA5/2Fq1rSbiSiQVRBKEzawAuA241t2rEoaagMSmnjqg8ADGmsLP27tfW34KlCbchu/j5YiISCt1jc189p43qapv4ujRffn6WROiLkkyhGaEpTOiWsDx/wFz3P3RVse3AwMSPi8GGvZ3zN1rzaw2HKts4357cPd6EoKzFnoXEem8HzzyDgs3VtK/dx6//sQR5GZH3YknmaJlRrhcM8Kyb1EF4YuAAWa2M/y8EPhY+PHLCedNA9aHH88hWEniD3sZe6aNsdfDseUJY/GL80REpIv988113Dd7LWbwywunMbi0IOqSJIOMDpdQW7WtGnfXhJbsVVQ/op8ETAYOD28PA/8NjAROMLMzzCyXoH/4yfA+/wAuNLMpZlYEfDFh7AHgs2Y2zMwGEaw0kTj2zfDit4OBjyaMiYhIF1q6uYrvPPg2AF86fTwnji+LuCLJNMP79iLLoKahma27tISa7F0kM8Lu/p4L0MxsF1Du7uVm9hXgMWAXsBO4PLzPfDP7JcEMbx2wDPht+BCPABeExyCYGf5n+PFtwHkEF73lA3e4+xvd8sJERDJYdX0T1/7lDWobmzlpfBlfOG181CVJBsrPyWZon16s21HL6m01DCzWbySkfUmxybu7X57w8a1m9iQwEZjl7rsSxr5jZvcAw4CZ7t4QHnfgUjP7FdA7HPNwrN7M3gecANS7++yeel0iIpnC3bn+wQUs31rNoJJ8fv7xw8nO0q+kJRqj+/dm3Y5aVpVXc/ToflGXI0ksKYJwa+6+kvcueZY4thBY2M7YnHaOx4BZXVagiIi8xz2vreGheRvIzjJuuegIyoryoy5JMtio/oW8+C6s1hJqsg+6jFdERA7IgnUV3PBIMD/xzfdP0AycRG50/90XzInsjYKwiIjst4qaRj577xs0NMd436RBXH3S2KhLEmFUuISaZoRlXxSERURkv7g7X3tgPmu31zKiXy9uumCqlqqSpNB6CTWR9igIi4jIfvn9rBX8Z+Fm8rKz+O1FR1LaKzfqkkQAGNkvmBGuqmtiR01jxNVIMlMQFhGRTpuzajv/+0SwN9F/f2gSU4aXRlyRyG4FudkMCTdyUZ+w7I2CsIiIdEr5rno+f++bNMec8w4fysXHjoy6JJE97O4TVhCW9ikIi4hIhzXHnC/fP4/NlfUcNLCIn5w/RX3BkpRaVo4o1wVz0j4FYRER6bBfPbOMF98tp1duNr+7+Ah65yflcvQijAqDsGaEZW8UhEVEpENeWLqVXz0b7GT/k49MZvyg4ogrEmnf6LA1YpWWUJO9UBAWEZF92lhRy5f/Og93+MQxIzl/2vCoSxLZK80IS0coCItI0np4/gZueGQh63ZoRidKjc0xvnDvXLZXNzBpSAnf+9CkqEsS2af4xXI7ahqp0BJq0g4FYRFJSu7Odx9cwB9fWslpN83kx48uZEd1Q9RlZZy122v46t/m8/rqHRTn5/C7S46gIDc76rJE9ql3fg4DivMBWL1ds8LSNl3lICJJad2OWirrmgBoaI7x+1kruX/OWj57ykFcccJohbFuNm/tTn4/awWPL9hILNyY68YLDmv5dbNIKhjdv5CtVfWs2lbDYcP7RF2OJCHNCItIUlq0sRKAiYOL+dMVRzNxcDFVdU387xOLOeXG5/nbnLU0x5J769T6pmYenr+BP764krXbk7+9IxZznnpnEx+79RU+/JuXePStIASfNL6Me68+lvdPHhJ1iSKd0tInXK4ZYWmbZoRFJCkt3lQFwKQhJZwyYSAnjR/AQ/PWc/NTS1m/s5Zv/OMt7nhxBd84ayKnHzIwqdayXbu9hntnr+Gvc9ayPWznuOHfCzl6dF8+PG0Y50wZQp/CvIir3K22oZl/vLmOP7y4kpVhYMjNNs6dOoyrThrDIUNKIq5QZP9o5QjZFwVhEUlK8RnheAjLzjI+csRwPjBlCHe/sppbnnuXpZt3cdWfX+eY0f341gcmcsTIvpHV2xxzXli6lbtfXc1zS7bg4WT14JICRvUvZPaq7cxZtYM5q3bw/Yff4dQJAzl/2jBOnTgwsjaPrVX13P3KKu5+dTU7wouJSgpyuPi4UVx+/GgGlRREUpdIV9HKEbIvCsIikpRaB+G4gtxsrj55LB87agS/m7mcO19ayexV2/nIb1/m7MmD+dpZExg3oKjH6txe3cDfXl/LPa+tZu322pbjJ40v4+JjR3HGIQPJyc5iU0UdD89fz4NzN7BoYyVPLdzMUws3U1KQwzmHDeHDhw/j6NH9yMrq/pntd7dUcceslfxz7noammIAjOjXiytPGMMFR43QJhmSNlp2l9OMsLTD3JO7xy5qZlYCVFRUVFBSol8PivSE6vomJn//Sdzh9e+eQVlRfrvnbthZyy+eXsoDb6wj5sHM8YVHj+BLp49nYDfNaLo7c9fu5C+vrObfCza2hMmSghwuOGoEFx87krF7CeOLN1Xy4Nz1PDR3A5sq61qOD+vTi/MOH8r504Z1+WYV7s4rK7bx+xdW8NySrS3HDx/Rh2tOHstZhw4muwdCuEhPqqhtZOoPngLg7R+cRZF+yEtblZWVlJaWApS6e2VH76cgvA8KwiI9743VO/iv373MgOJ85nznjA7dZ8mmKm58cjFPL9oCQK/cbK4+aQxXnzyW4oLcLqmrpqGJh+Zt4O5XVrNw4+5/Z6cMK+XS40bxoalD6ZXX8TaHWMx5deU2/jV3PY8v2ERVfVPL2KFDSzh/2jDOnTr0gAJ9Y3OMxxZs5PYXVvDOhqBmMzhz0iCuPmksR47qm1T91SJd7Ygf/oft1Q08+sUTOXRoadTlSDdREO4mCsIiPe+e11bznQff5uSDB/DnTx3Tqfu+tmIbP318MfPW7gSgf+88vnDaQVx07CjycvZvoZx3t+ziL6+u5h9vrqMqXNItPyeLD00dyqXHjWLqiD779biJ6hqbeWbRFh6cu57nl2yhKVwRI8vghIPKOH/aMM46dHCH2xYq6xq5f/Ya7nxpFRsrglnngtwsLjhyBJ86cQxjyrQMmmSG83/7EnPX7OS3Fx/BB6Zo5ZN0tb9BWL8jEJGks7s/uPPtAceO7c+Dnz2eJ97exI1PLmFFeTXff2Qhd768iq+dOYFzpgzpUB9uY3OMpxdu5u5XV/Py8m0tx0f1L+SSY0fx0SOH07d31638UJCbzTmHDeGcw4YEs1dvbeDBuet5c81OZi0rZ9aycnrlvs2Zhw7iw9OGcdJBZeRk7xns1++s5c4XgzWXd4UzzGVF+Vw2fRSXHDeqS2sWSQVj+vdm7pqdLSuiiCRSEBaRpLNo4+6l0/aHmXH2lCGcMWkQf52zll88vYzV22r4wn1zuf2FFXz77Ikcf1BZm/fdVFHHfbPXcP+cNWyurAeCWdnTDxnEJceN4qSDyrr9grZ+vfO4dPpoLp0+mtXbqvnX3A38a956VpZX89C8DTw0bwNlRXl88LCgn/iw4aUsWF/B72et5LEFG1vWVx4/sIirThrDeYcP0wYkkrG0coTsjVoj9kGtESI9KxZzpnz/SaobmnnyyyczYfCBXzRWXd/EH15cyW0zl1Pd0AzAyQcP4Fvvn8ikoSXBhWTLt3H3q6t5auHmliBZVpTHhUeP5BPHjmRYn14HXMeBcHfmr6vgX3PX8/D8DS3rEwMMKM5na1V9y+fHj+vP1SePZcb4AT2yCoVIMnto3nq+dP88jhnTj799enrU5Ug3UWuEiKSFtTtqqG5oJi87i7EDuqaPtXd+Dl88fTwXHTuSW559l7+8upoXlm5l1rKtnDVpMMu2VLF86+7ZomNG9+OS6aN4/6GD97uvuKuZGYeP6MPhI/rwnXMOYdayrTw4dwNPvbOJrVX15GQZH5o6lCtPHMPkYbogSCROM8KyNwrCIpJU4m0R4wcVkdtGD+yBKCvK5/vnHsoVJ4zmxieX8O+3NvLEO5sA6J2XzUeOGM4lx43qklno7pSbncVpEwdx2sRBVNU18uaanRw8qIghpdHOWosko/jucpsr66lpaKIwT9FHdtPfBhFJKu1tpNGVRvXvzS0XHcE1J+/kwbnrGVvWm/OPGJ6Sa4wWF+Qy4+ABUZchkrT6FOZR2iuXitpG1myvYeJgtTnKbqn3r76IpLWeCMJxhw3vw2HD+3T784hItEb3L2T+ugpWlSsIy3slR/ObiEho0ab9XzpNRKQt6hOW9igIi0jSqKprZO32WgAO0ayNiHSReJ/wqm01EVciyUZBWESSxpJNwYVyg0sKtPGDiHQZzQhLexSERSRpHMiOciIi7RldFswIr9aMsLSiICwiSWNhuHRaT1woJyKZIz4jvKGilrrG5oirkWSiICwiSaMnV4wQkczRv3ceRfk5uMO6HZoVlt0UhEUkKTTHvKVHWK0RItKVzIxR8QvmyhWEZTcFYRFJCmu211Db2Ex+Thaj+3fN1soiInHxf1dW6YI5SaAgLCJJId4WMWFwMTldvLWyiEh8RlgXzEki/W8jIkmhpT9Y6weLSDfQjLC0RUFYRJKClk4Tke6kGWFpi4KwiCSFReHSaRO1YoSIdIPRZcGM8LodNTQ0xSKuRpKFgrCIRK6ippH1O7W1soh0n4HF+RTkZhFzWv69EVEQFpHILd4UtEUM69OL0sLciKsRkXRkZuoTlj0oCItI5NQfLCI9oaVPuFxBWAIKwiISuUXaWllEesDuGWFdMCcBBWERidyiTdpaWUS636gwCK9Wa4SEFIRFJFJNzbGWrZUnDlZrhIh0n9FaQk1aURAWkUit2lZDfVOMXrnZLbM1IiLdIb6E2todNTQ1awk1URAWkYglbq2cnWURVyMi6WxwSQF5OVk0NjsbdtZFXY4kAQVhEYnU7hUj1B8sIt0rK8sY1S9oj9ASagIKwiISsXgQnqSl00SkB+iCOUmkICwikdLWyiLSk+IXzGkJNQEFYRGJ0I7qBjZVBn16WjFCRHrCqDLNCMtuCsIiEpn4+sEj+vWiuEBbK4tI99OMsCRSEBaRyLTsKDdYbREi0jPiu8ut2VZDc8wjrkaipiAsIpHRihEi0tOGlBaQm200NMdaWrMkcykIi0hkdgdh9QeLSM/Iyc5iRN9wh7ly9QlnOgVhEYlEU3OMZZt3AZoRFpGeNUp9whJSEBaRSKwor6ahOUbvvOyW2RkRkZ6gtYQlTkFYRCIRb4uYOKSELG2tLCI9aPfKEQrCmU5BWEQisVD9wSISkd1rCas1ItMpCItIJFqWTlN/sIj0sPgSaqu2VeOuJdQymYKwiERicbw1QmsIi0gPG9anF9lZRl1jjC1V9VGXIxFSEBaRHrdtVz1bquox09bKItLz8nKyGNanFwCrtIRaRlMQFpEeF2+LGNWvkN75ORFXIyKZKL6EmvqEM5uCsIj0OO0oJyJRS+wTlsylICwiPU5BWESiphlhAQVhEYnAok1Ba4T6g0UkKpoRFlAQFpEe1tAU490tWjpNRKI1umz3jLCWUMtcCsIi0qOWb91FY7NTXJDD8L69oi5HRDLU8L6FmMGu+ia2VTdEXY5EJNIgbGZ9zOxYM+sbZR0i0nNa+oMHl2CmrZVFJBoFudkMLQ1+GF+t9oiMFVkQNrMLgFXAHcC68HPMbLKZzTGzHWZ2oyX8T2lmM8xskZmVm9l1rR7vo2a22sw2mNknWo19zsw2m9kKMzut+1+diLRnkbZWFpEkEb9gblW5LpjLVJEEYTMrBX4LnOzuU4DPATeaWT7wCPAGcBQwCbg8vM8A4GHgPmA6cLGZnRqOTQbuAX4InAXcYGYTwrGzgJuAa4BLgDvMrH/PvFIRaS2+hvBE9QeLSMRGhRfMaUY4c0U1I1wCfNnd3wo/fxPoD5wNlALXufty4HrgyvCci4ENwA/dfRlwQ8LYVcBz7n6Huy8AbgEuDceuBe5y94fc/WXgIeD8bn11ItKuxZu0dJqIJIcx4QVzK7WEWsaKJAi7+1p3vwfAzHKBrwAPAlOBV909/jfyLYJZYcKx53z3pZ2zgSMTxp5NeIqOju3BzPLNrCR+A/T7W5EusqWqjvJdDWQZTBikt5aIREszwhL1xXJTgU3A+4EvEswUr4yPh6G3ObyY7j1jQCUwNPx4f8fa8m2gIuG2rlMvSkTaFW+LGF3Wm1552RFXIyKZLr6W8Mryai2hlqGiXj7tLeBMYBnBRXNNQH2rc+qAwjbG4sc5gLG2/JSgPSN+G96xlyIi+6Id5UQkmYzsF8SBqromdtY0RlyNRCHSIOyBN4DLgI8A24EBrU4rBhraGIsf5wDG2qqp3t0r4zegqlMvSkTaFQ/CkxSERSQJ9MrLZnBJAaAd5jJVVKtGzDCzGxMONQAOLCJYESJ+3hggnyDMzkkcA6YB68OP93dMRHrQ4o3aWllEkkt8CbXVumAuI0U1I7wUuMbMrjGzEcBPgKeAx4ASM7siPO964Gl3byZYOu0EMzsjvMDuG8CT4Xn/AC40sylmVkTQbxwfewD4rJkNM7NBBCtNxMdEpIfUNzWzfOsuQK0RIpI84n3CmhHOTFGtGrER+CjwJeAdgp7dT7p7E8FSaLeYWTlwHvDN8D7lBKtLPAZsBiYAPwrH5gO/BF4nmO1tJlinGIJ1iZ8h6ENeCcwF/tntL1JE3mPZ5l00xZzSXrkMKS2IuhwREQBGlWlGOJPlRPXE7v4f4NA2jj9sZuMIljh71d23JYzdamZPAhOBWe6+K2HsO2Z2DzAMmOnuDeFxBy41s18BvcMxXRoq0sMSd5TT1soikiw0I5zZIgvCe+Pum4BH2xlbyXuXQ0scWwgsbGdsTpcVKCKdFl86TW0RIpJM1COc2TrdGmFmOWZ2+T7OOdnM8va7KhFJOy07yg1WEBaR5BHfVGN7dQMVtVpCLdPsT4+wA/9nZteZ2YVmdoSZtayMH16Qdj9wblcVKSKpzd21hrCIJKWi/BzKivIBWKNZ4YzT6SAcruAAMBD4APArYJOZ/dPMPgQ8DTzk7g90XZkikso2V9azo6aR7Cxj/KCiqMsREXmP0WF7hPqEM0+Hg7CZTTWz+I5sO939W+7+SXc/ETiIYCvjh4Ct7n5tN9QqIikqPhs8tqw3BbnaWllEkku8PWK1gnDG6cyM8O+AbWY2Eygys1PN7Goz+xPwNrAFODUcu6TrSxWRVLVQbREiksR2zwirNSLTdHjVCHc/3szKgNOBcwhmf3sBNwGT3L0KwMz+C3jVzJ5393XdULOIpJjFm8Id5YZoRzkRST6jyjQjnKk60xrxKeD9QDbBhhXDgL8DAwh2iRsWnvpt4A7gI11bqoikKl0oJyLJTDPCmasz6wjPAz5PEJ7rgNuAje7+VTOrBr5oZvMJZom/HN/QQkQyW11jMyvCrZUnKQiLSBIa1S+YEd5aVU91fRO985NymwXpBp3pET4RmAPsItjZrQpYZsEWUVuA8UAu0I8k3ahDRHre0s1VxBz69c5jYHF+1OWIiOyhtDCXvoW5gDbWyDSdCcJTw9to4GRgHHAI8BJQEN6KgWeAn3ZplSKSsrS1soikAq0ckZk6E4SvBaqBp4DZwAiClSS+AZQStE7kAN8CTjez4V1aqYikpJatlbWjnIgkMfUJZ6bOBOFvAnnADmARcBXBxXLzge3AkcB33D0G/AX4ZNeWKiKpKD4jPFH9wSKSxDQjnJk608v7Y3ePhTO9i9z9dTO7iWAFiV+6+w6CtgiAe4CdXVuqiKSa926trKXTRCR5jS7T7nKZqDPrCMfCP9cB68KP7wqHb4yfZ2bm7mu7skgRSU0bKuqorGsiJ8s4aKC2VhaR5LV7RlitEZmkw0HYzPKAde4+sJ3xDxNss3yyma109x93TYkikqoWbQhmgw8aWER+jrZWFpHkNToMwhsr6qhrbNZ28Bmiwz3C4brADWZ2gZm9z8yOMbNDzOza8JQTgTOBpwn6iUUkw2kjDRFJFX0LcykpCOYH12zXrHCm6MzFcgBDga8Dw4FHCWaAjw/HKoFe7n4/sK3LKhSRlNWytfJg9QeLSHIzM0aHWy2vLFefcKboUBA2sxwzuwhY6+7HAH8i2DyjFugdnrYr4WMREc0Ii0hK0coRmaejM8KTgJuBvmZ2rrs7EL8gLj7VU4eCsIiEahqaWBn+Z6IgLCKpQGsJZ54OXSzn7m+Z2TDgbOB7ZnYBwcoRxu4g3MDuINyrqwsVkdSyZFMV7lBWlM8Aba0sIilAM8KZpzPrCGcBXwaOAyYC3wbuBMab2QvAYGComc0l2IFORDJYy45yWj9YRFJEy4xwuWaEM0VnVo1oAsaHt1OACuBhoAn4A3A70Aj8HDirqwsVkdSyeJP6g0UktcRnhDdU1FLf1BxxNdITOrtqxDBgJsGscDXwBGDhxhp3A+7uf3b3d7u2TBFJNdpRTkRSTVlRHr3zsnGHtdtroy5HekBHV40YbGa/Ada7+2B3/yRBEO4F5IWn7UK9wSJCsLXy4pbWCM0Ii0hqMDP1CWeYjs4IDwBOBfqb2UfDYwYUAk1m1otg1Yi8du4vIhlk3Y5aquqbyMvOYtwAba0sIqljdJlWjsgkHQrC7r7A3ScBVwA3m9m/gVeA1wlWj+jl7s0AZlbYXcWKSGpYuHH31sq52Z3twBIRiY5mhDNLZ1aNwN0fMLNngIvc/anw8KSEU24nmCkWkQymtggRSVVaSzizdCoIA7j7DuA37Yxde8AViUjK04VyIpKqNCOcWQ74d5ZmNsTM9LtPEWmxSEuniUiKGh0G4XU7amlsjkVcjXS3Ds8Im9khQC0QIwjQ5u4rgfVAs5ltAFYBy4D/0RJqIplpV30Tq8NfKSoIi0iqGVicT0FuFnWNMdbvqGV0We9930lSVmdmchcALwPLCS6U+1d4/B1gEPBfBC0TZcD/dV2JIpJKloSzwYNK8unXWwvJiEhqycoyRvULwu8qtUekvc4E4bXuPhRY5+5DEu47GDjE3V93978B1wNHd3GdIpIiFulCORFJcaPCC+ZW64K5tNeZIOzt/GnAvWb2x3DptM3ApV1Un4ikmPiFchMHKwiLSGqKt0NoRjj9dcVFbhuAQ4Bs4Dmgzt2f7oLHFZEUpBUjRCTVaUY4c3RFEB4NnOjulwFvAT/vgscUkRQUizmLNwWtEZPUGiEiKSq+coRmhNNfp9cRTlBmZiOBSuBGMysCPgvMNbPJ7v52l1QoIiljzfYaahqaycvJYoyutBaRFBWfEV67vYbmmJOdpb3C0tWBzAiXArOB7cApwE+BYcCPgM8dcGUiknIWhytGTBhUTI62VhaRFDWktBd52Vk0NjsbdtZGXY50o878TzXAzP6Y8OcK4FPACKAY+CZwM/AgcHpXFyoiyW9huGLExMHqDxaR1JWdZYzo1wtQn3C660wQ/g7BDPDXgNeB/3X3x4AfA793938BBwH9gXO7uE4RSQG7L5RTf7CIpDb1CWeGDvcIu/uv2hn6P+DjZjYC+KS7byBYSUJEMoyCsIiki1FhEF6tIJzWDuRiOQDcPWZmp7l7FbC2C2oSkRRUWdfIuh1BL51WjBCRVDemLLhgbpVaI9Jap65mMbMSM1ttZlnh50cDhCE4fk4vM/u3mQ3u2lJFJJktDvuDh5YWUFqYG3E1IiIHJj4jvKpcM8LprLOXde8C+oezwB8FXjCzoa3OORg4m907z4lIBoivGKG2CBFJB/Ee4dXba4jFFGnSVaeCsLvHgBozGwXcDvw3sLHVaScC77j75q4pUURSQcvWytpRTkTSwNA+BeRkGQ1NMTZV1kVdjnST/Vno04DfA48RzP7OMrNxCeP/BdzTBbWJSAqJL52mGWERSQc52VmM6BfvE1Z7RLra3xXvvwlcC1wPvAbMNrPzzewY4Ajg1i6qT0RSQHPMWaLWCBFJM/Ed5rSWcPrq0KoRZtYHuALoDbi7zw2HqoCvmtmDwN8IZou/4+4V3VCriCSpVduqqWuMUZCb1dJXJyKS6oJ/z7ZqRjiNdXRG+Czgh0B7Uz0bgC3AAOD5Ay9LRFJJfMWICYNLyM6yiKsREekaLTPC5ZoRTlcdDcIPA0Pd/RuAmdlIMys0s8FmdiMwH3gc+CpwRzfVKiJJqmUjDW2tLCJpRLvLpb8OtUa4ey1Qm3DoVoLl0W4FpgCnuvvrAGZ2pZmd5+4PdXWxIpKctKOciKSjxB5hd8dMv/FKN/tzsZwTXCg3AfiAu78/HoJDvwO+1BXFiUhqUBAWkXQ0vG8hWQa1jc1sraqPuhzpBvsThHPcfTVwJvBRM/tcq/FHgJPNbNABVyciSW9nTQMbKoI1NrWGsIikk7ycLIb17QVoq+V01dktlouAbQDuvgK4hlYXx7n7OuBdYHSXVCgiSW3xpuBCueF9e1FSoK2VRSS9qE84vXV2Z7ld7j4+4fMH3f2dNk493N1fO+DqRCTptewoN1htESKSfnb3CSsIp6P93VBjr9xdexGKZIh4EJ6ktggRSUO7Z4TVGpGOuiUIi0jmWKStlUUkjY0Kg7BmhNOTgrCI7Lem5hhLNisIi0j6Gp2wqYa7R1yNdDUFYRHZb6u2VdPQFKN3XjYj+xVGXY6ISJcb0a8QM6iqb2J7dUPU5UgXUxAWkf22sGVr5WKytLWyiKShgtxshpQUAOoTTkcKwiKy31pWjFBbhIikMfUJpy8FYRHZb9pRTkQyweiyoPVLM8LpR0FYRPablk4TkUygGeH0pSAsIvtle3UDmyvrAZigzTREJI3FV47QjHD6URAWkf2yOJwNHtW/kKL8nIirERHpPpoRTl8KwiKyX97ZEPYHazZYRNJcfJvlnTWN7KzREmrpREFYRPbLvLU7AThsRGm0hYiIdLPCvBwGFucDsFrtEWlFQVhE9ks8CB8+ok+kdYiI9ITRZUF7xCq1R6QVBWER6bStVfWs31mLGUwZphlhEUl/LRfMlWtGOJ0oCItIp721bicABw0oorggN9piRER6gC6YS0+RBWEzO8/MVphZk5nNM7NDwuOTzWyOme0wsxvNzBLuM8PMFplZuZld1+rxPmpmq81sg5l9otXY58xsc/h8p/XMKxRJX/G2iKlqixCRDDG6v1oj0lEkQdjMxgF3At8ChgFLgTvMLB94BHgDOAqYBFwe3mcA8DBwHzAduNjMTg3HJgP3AD8EzgJuMLMJ4dhZwE3ANcAl4fP075EXKpKm1B8sIpkmvnKELpZLL1HNCB8CfMvd/+bum4HfAdOAs4FS4Dp3Xw5cD1wZ3udiYAPwQ3dfBtyQMHYV8Jy73+HuC4BbgEvDsWuBu9z9IXd/GXgIOL/bX6FImnJ35isIi0iGiQfhbdUNVNY1RlyNdJVIgrC7/9vdb084NAFYBkwFXnX3+I9bbxHMChOOPefuHn4+GzgyYezZhMfr6NgezCzfzEriN0B7x4okWLWthsq6JvJzspgwWG8PEckMxQW5lBXlAbBGs8JpI/KL5cwsD/gqcCtQAqyMj4Wht9nM+rYeAyqBoeHH+zvWlm8DFQm3dZ17RSLpbd7aHQBMHlZKbnbk/4SIiPSYUeoTTjvJ8L/YD4Bq4A6gCahvNV4HFLYxFj/OAYy15acE7Rnx2/AOvg6RjDB/bQUAU4f3ibYQEZEepj7h9JMT5ZOHKzh8DjjO3RvNbDswudVpxUADsB0Y0MZxDmBsD+5eT0JwTli0QkRIXDFC6weLSGZpWTmiXDPC6SLK5dPGEKwA8Tl3XxgenkOwIkTiOfkEYfY9YwQX161v636dGBORTmhoirFwQyWgC+VEJPNoRjj9RLV8Wi/g3wQrODxoZkVmVgTMAkrM7Irw1OuBp929mWDptBPM7AwzywW+ATwZnvcP4EIzmxI+zhcTxh4APmtmw8xsEMFKE/ExEemERRsraWiO0bcwl5H99tZhJCKSfrSWcPqJqjXiTILVICYBVyccH0OwFNp9ZnYjEANOAXD3cjP7CvAYsAvYSbjGsLvPN7NfAq8T9AAvA34bPuYjwAXhMYBngH92z8sSSW/zwx3lpo7oo7YhEck48SC8paqemoYmCvMi7TCVLhDJd9DdHwLa+190VbjhxpEES6ltS7jfrWb2JDARmOXuuxLGvmNm9xBs0DHT3RvC4w5cama/AnqHY46IdFpLf7AulBORDFRamEufwlx21jSysryaQ4fqWolUlwyrRuzB3Te5+6OJIThhbKW7P54YghPGFrr7f+IhuNXYHHd/XiFYZP9pRzkRyXSTw/D7yPyNEVciXSEpg7CIJJ+K2kZWbA364qYqCItIhrrs+NEA3PPqau0wlwYUhEWkQxasC9YPHtmvkH698yKuRkQkGqdPHMj4gUVU1Tdx72troi5HDpCCsIh0SOKFciIimSory7jm5LEA/PHFldQ3NUdckRwIBWER6ZC5a3YCMHW4Lg4Rkcx23uHDGFxSwJaqev41V1sTpDIFYRHZJ3dvuVBu2sg+kdYiIhK1vJwsrjxxDAC3vbCCWEzX4acqBWER2aeNFXWU76onO8u0XJCICPCJY0dSUpDDiq3VPLVwc9TlyH5SEBaRfZofzgZPHFxMQW52tMWIiCSBovwcLp0+CoBbZy5Hq7OmJgVhEdmnlo00dKGciEiLy48fQ15OFvPW7mT2yu1RlyP7QUFYRPZJG2mIiOxpQHE+Hz1yOBDMCkvqURAWkb1qjjkL1gdrCCsIi4i81zUnjSXL4LklW1m8qTLqcqSTFIRFZK+WbamipqGZ3nnZjBtQFHU5IiJJZXRZb86ePASA22euiLga6SwFYRHZq/iFcocN70N2lkVbjIhIEvr0jGCDjYfnb2D9ztqIq5HOUBAWkb2atzZoi9CFciIibTtseB+OH9efpphzxyzNCqcSBWER2av5LRfKaf1gEZH2fGbGOADun72WHdUNEVcjHaUgLCLtqm1oZsnmKkAzwiIie3PS+DImDSmhtrGZu19dHXU50kEKwiLSrrc3VNAccwaV5DOktFfU5YiIJC0za+kV/tPLq6htaI64IukIBWERaVe8LWLq8D6R1iEikgrOmTKEEf16sb26gb+/sTbqcqQDFIRFpF3aUU5EpONysrO4+qRgVvj2F1bQ1ByLuCLZFwVhEWmXdpQTEemcC44cQb/eeazbUctjb2+KuhzZBwVhEWlT+a561u2oxQymDNeKESIiHdErL5vLpo8G4Nbnl+Pu0RYke6UgLCJtemvdTgDGDSiipCA32mJERFLIJ6ePolduNgs3VjJrWXnU5cheKAiLSJvmrdkJ6EI5EZHO6ts7jwuPGQHArTOXR1yN7I2CsIi0ad66YEc5baQhItJ5V500luws4+Xl21p+wybJR0FYRPbg7gk7yvWNthgRkRQ0rE8vzp06FIDbZmrb5WSlICwie1i9rYaK2kbycrKYMLg46nJERFJSfIONx9/eyKry6oirkbYoCIvIHuLLph06tIS8HP0zISKyPyYOLuHUCQOIOdw+S7PCyUj/w4nIHrR+sIhI1/jMjHEAPPDGOrZW1UdcjbSmICwie5gfXtihICwicmCOGdOPw0f0oaEpxp9eXhl1OdKKgrCIvEdDU4x3NlQCWjpNRORAmVnLrPDdr6xmV31TxBVJIgVhEXmPxZsqaWiK0acwl1H9C6MuR0Qk5Z05aRBjB/Smsq6J+2evibocSaAgLCLvEV82berwPphZtMWIiKSBrCzj0ycHK0jcMWslDU2xiCuSOAVhEXmPeWuDjTSmqj9YRKTLfHjaMAYW57Opso6H5q2PuhwJKQiLyHvsvlBOO8qJiHSV/JxsPnXiGABue2EFsZhHXJGAgrCIJKisa2T51l2ALpQTEelqFx07kuL8HN7dsotnF2+JuhxBQVhEEixYV4E7jOjXi/5F+VGXIyKSVkoKcrn4uFEA3DpzecTVCCgIi0iCeQkXyomISNf71AmjycvO4vXVO3h91faoy8l4CsIi0kI7yomIdK+BJQV85IhhgGaFk4GCsIgA4O67Z4QVhEVEus01J4/FDJ5etIVlm6uiLiejKQiLCACbKuvYWlVPdpYxeahWjBAR6S5jBxRx1qTBQLCChERHQVhEgN0baUwYVEyvvOxoixERSXOfnhFssPHQvPVsrKiNuJrMpSAsIgDMVVuEiEiPmTayL8eO6Udjs/OHWSujLidjKQiLCLB7RniagrCISI/4zCnjALhv9hoqahojriYzKQiLCM0xZ8E6ba0sItKTTjl4ABMHF1Pd0MxfXlsddTkZSUFYRFi+dRfVDc0U5mVz0MCiqMsREckIZsZnZgSzwne+tJK6xuaIK8o8CsIiwrw1OwGYMqyU7CyLthgRkQxyzmFDGNanF+W7GnjgjXVRl5NxFIRFhHnrdgJw+Mg+kdYhIpJpcrOzuOqkMQD8ftYKmmMecUWZRUFYRFoulDtcWyuLiPS4jx89gr6FuazeVsMTb2+KupyMoiAskuFqG5pZvCnY2UgXyomI9LzCvBw+OX00EGy77K5Z4Z6iICyS4d7ZUEFzzBlQnM+Q0oKoyxERyUiXHT+agtwsFqyv4JXl26IuJ2MoCItkuHnxtogRfTDThXIiIlHo1zuPjx81AoDfzVwecTWZQ0FYJMPND9cPPlxtESIikbrqpLFkZxmzlpXz9vqKqMvJCArCIhlu3todAEzVhXIiIpEa0a+QDx42BIDbXlgRcTWZQUFYJINt21XP2u21AEwZXhpxNSIi8umTgw02Hn1rA2u21URcTfpTEBbJYG+FbRHjBvSmtFduxNWIiMikoSXMOHgAMQ/WFZbupSAsksHiF8pp2TQRkeQR33b5b6+vpXxXfcTVpDcFYZEMlrhihIiIJIfjxvZj6og+1DfF+NNLq6IuJ60pCItkKHdnfri1si6UExFJHmbGtTPGAvDnV1axq74p4orSl4KwSIZas72GnTWN5GVncciQkqjLERGRBO+bNJixZb2prGvi/tlroi4nbSkIi2SoeFvEpKEl5OXonwIRkWSSnWV8OpwVvmPWShqaYhFXlJ70v59IhlJ/sIhIcvvwtGEMKslnU2Ud/5q3Pupy0pKCsEiGmq8gLCKS1PJzsvnUCWMAuG3mcmIxj7ii9KMgLJKBGptjvL2hEtDSaSIiyeyiY0dSXJDD8q3VPL1oc9TlpB0FYZEMtHhjFQ1NMUoKchjdvzDqckREpB3FBblcctwoAG6duRx3zQp3JQVhkQw0L75s2og+mFm0xYiIyF5dccJo8nKyeHPNTuas2hF1OWlFQVgkA8X7g6epLUJEJOkNLC7go0cOB+B3z78bcTXpRUFYJAPN19bKIiIp5ZqTxpJl8NySrSzeVBl1OWlDQVgkw1TVNfLu1l0AHKYd5UREUsLost6cPXkIALfNXBFxNelDQVgkwyxYV4E7DOvTiwHF+VGXIyIiHfSZGeMAeHj+BtbtqIm4mvSgICySYeIXyh0+sk+kdYiISOdMGV7KiQeV0Rxz7pi1Mupy0kKkQdjMysxspZmNTjg22czmmNkOM7vREi5pN7MZZrbIzMrN7LpWj/VRM1ttZhvM7BOtxj5nZpvNbIWZndbtL0wkibVspKG2CBGRlBOfFb5/zhq2VzdEXE3qiywIm1kZ8G9gdMKxfOAR4A3gKGAScHk4NgB4GLgPmA5cbGanhmOTgXuAHwJnATeY2YRw7CzgJuAa4BLgDjPr3+0vUCRJzdOFciIiKeuEg/ozeVgJdY0x7np5VdTlpLwoZ4TvB+5tdexsoBS4zt2XA9cDV4ZjFwMbgB+6+zLghoSxq4Dn3P0Od18A3AJcGo5dC9zl7g+5+8vAQ8D53fSaRJLapoo6NlfWk51lTB5WEnU5IiLSSWbWMit81yurqGloirii1BZlEL7a3X/V6thU4FV3j3eAv0UwKxwfe853b6kyGzgyYezZhMfp6NgezCzfzEriN6C4E69JJKnFZ4MPHlRMYV5OtMWIiMh+OXvyEEb1L2RnTSP3z14bdTkpLbIg7O5tdXmXACsTznGg2cz6th4DKoGhbd2vE2Nt+TZQkXBbt6/XIpIq4kH48BGl0RYiIiL7LTvLuObksQD84cWVNDbHIq4odSXbqhFNQH2rY3VAYRtj8eNt3a+jY235KUF7Rvw2vOPliyS3lo00dKGciEhK+68jhlNWlM/6nbU8Mn9D1OWkrGQLwtuBAa2OFQMNbYzFj7d1v46O7cHd6929Mn4Dqjr7IkSSUXPMWbC+AtDSaSIiqa4gN5srThgNBBts7O4clc5ItiA8h2BFCADMbAyQTxBm3zMGTAPWt3W/ToyJZIwVW3exq76Jwrxsxg9U67uISKq75LhRFOXnsGRzFc8t2RJ1OSkp2YLwC0CJmV0Rfn498LS7NxMsnXaCmZ1hZrnAN4Anw/P+AVxoZlPMrAj4YsLYA8BnzWyYmQ0iWGkiPiaSMeaGbRGTh5WSnWV7P1lERJJeaa9cLj52JAC/e355xNWkpqQKwu7eRLAU2i1mVg6cB3wzHCsHvgI8BmwGJgA/CsfmA78EXieY7W0Gfhs+7CPAM8Aygovm5gL/7JlXJJI8WjbS0PrBIiJp41MnjiEvO4s5q3bwxurtUZeTciIPwu5u7r4q4fOHgXHAZcAh7r4wYexWggB8MXCYu29OGPsOQdvDR4Hj3b02PO7ufikwA/gAcK6rkUYy0Pz41soKwiIiaWNQSQHnTxsGwO+eXxFxNakn8iDcFnff5O6Puvu2NsZWuvvj7r6rjbGF7v4fd9/jYjh3n+PuzysESyaqa2xm8cbguk/tKCcikl6umTEWM3h60WaWbdY1/p2RlEFYRLrWOxsqaIo5ZUX5DC0tiLocERHpQuMGFHHmpEEA3DpTs8KdoSAskgHmrQ2XTRtRipkulBMRSTfxbZcfmreeDTtrI64mdSgIi2QAXSgnIpLepo3sy3Fj+9EUc/7wYlub90pbFIRFMkB8a2X1B4uIpK/4rPB9s9ews6bdvcMkgYKwSJrbXt3Amu01ABw2rE+0xYiISLeZcfAADhlSQk1DM3e/sjrqclKCgrBImosvmza2rDelhbnRFiMiIt3GzPjMjLEA3PnyKmobmiOuKPkpCIukOfUHi4hkjnOmDGFEv15sr27g72+sjbqcpKcgLJLm1B8sIpI5crKzuPqkYFb49hdW0NQci7ii5KYgLJLG3L1lRlhBWEQkM1xw5Aj69c5j3Y5aHl2wMepykpqCsEgaW7u9lh01jeRlZ3HIkOKoyxERkR7QKy+by48fDQQbbGhT3fYpCIuksXnhhXKHDC0hPyc72mJERKTHfHL6KArzslm0sZKZS7dGXU7SUhAWSWPz1uwE4PDhpdEWIiIiPapPYR6fOGYkALfOXB5xNclLQVgkjcWXTlN/sIhI5rnyxDHkZBmvrtjO3DU7oi4nKSkIi6SpxuYYb6+vALR0mohIJhrapxfnHT4M0KxwexSERdLUkk1V1DfFKCnIYXT/3lGXIyIiEYhvsPHUws0s37or4mqSj4KwSJpKXD84K8uiLUZERCIxflAxZxwyCHe4feaKqMtJOgrCImmqZf3g4X0irUNERKJ17SnBrPCDc9ezubIu4mqSi4KwSBqKxZw5q7YD6g8WEcl0R47qx9Gj+9LQHOOPL66MupykoiAskoYeeHMdq7bVUJSfw9Gj+0VdjoiIROwzM8YBcM9ra6iobYy4muShICySZirrGvnZE0sA+OLpB1FamBtxRSIiErVTJwzk4EFF7Kpv4i+vro66nKShICySZn79zDLKd9Uztqw3lx8/JupyREQkCWRlWcus8J0vraKusTniipKDgrBIGnl3yy7ufGkVAP/vQ5PIy9FbXEREAh+aOpRhfXpRvquef7y5LupykoL+lxRJE+7OD/+9kKaYc/rEgZw6YWDUJYmISBLJzc7iyhOD3xTe/sIKmmMecUXRUxAWSRPPLt7CzKVbyc02vvvBSVGXIyIiSejCY0bQpzCX1dtq+OY/3sr4MKwgLJIG6puaueHfCwH41IljGFOmneRERGRPhXk5/PjDU8jOMh54Yx1f+es8mppjUZcVGQVhkTTwxxdXsXpbDQOK8/nCaeOjLkdERJLYOYcN4ZZPTCMny3h4/gY+f+9cGpoyMwwrCIukuM2Vdfz62WUAfOv9EynKz4m4IhERSXZnTxnCrZccSV52Fk+8s4nP/OWNjFxJQkFYJMX97+OLqWloZtrIPpw/bVjU5YiISIo4Y9Ig7rjsKPJzsnh28Rau/vPr1DZkVhhWEBZJYW+s3sE/564H4PsfOpSsLIu4IhERSSUnHzyAP11xDIV52cxaVs4Vf5pNdX1T1GX1GAVhkRQViznff/gdAD521HCmjugTbUEiIpKSpo/rz58/dQxF+Tm8umI7n/zjbCrrMmMbZgVhkRT19zfWsmB9BcX5OXz9rIlRlyMiIinsqNH9+MtVx1JSkMMbq3dwyR2vsbOmIeqyup2CsEgKqqht5GdPLAHgS2eMZ0BxfsQViYhIqjt8RB/uu+Y4+hbm8ta6Ci76/Wts21UfdVndSkFYJAX96pllbKtuYOyA3nxy+uioyxERkTRx6NBS7r9mOmVF+SzcWMmFt7/Klqq6qMvqNgrCIinm3S1V3PXyKgD++4OTyMvR21hERLrOhMHF/PXTxzG4pIBlW3Zx4W2vsrGiNuqyuoX+BxVJIe7ODx5ZSFPMOeOQgZwyYWDUJYmISBoaN6CIv316OsP69GJFeTUfu+0V1m6vibqsLqcgLJJCnl60hVnLysnLzuK750yKuhwREUljI/sX8tdPH8eo/oWs3V7Lx297hVXl1VGX1aUUhEVSRF1jMz/890IArjxpDKPLekdckYiIpLvhfQv56zXTGTegNxsq6vjYba/w7pZdUZfVZRSERVLEH15cyZrtNQwqyefzpx4UdTkiIpIhBpcWcP8105kwqJgtVfVcePsrLN5UGXVZXUJBWCQFbKqo4zfPvQvAt86eSO/8nIgrEhGRTDKgOJ/7rjmOQ4eWUL6rgQtvf5W311dEXdYBUxAWSQH/8/giahqaOWJkHz58+LCoyxERkQzUr3ce9151HFNH9GFnTSOf+P2rzF2zI+qyDoiCsEiSe33Vdv41bwNm8P1zD8XMoi5JREQyVGlhLn+58hiOHt2XqromLrnjNWav3B51WftNQVgkiTXHnO8/8g4AHztyBIcN7xNtQSIikvGKC3K561PHcPy4/lQ3NHPZH2fz8rvlUZe1XxSERZLY319fy9vrKynOz+Hr758QdTkiIiIAFObl8MfLj2bGwQOobWzmij/N4fklW6Iuq9MUhEWSVEVtIz97cgkAXzpjPGVF+RFXJCIisltBbja3f/JIzjhkEPVNMa758xs89c6mqMvqFAVhkST1y6eXsb26gYMGFnHZ8aOjLkdERGQP+TnZ/O6SIzhnyhAammN89p43efStjVGX1WEKwiJJaNnmKu56ZRUA//3BSeRm660qIiLJKTc7i19eeDgfPnwoTTHnC/e9yYNz10VdVofof1eRJOPu/OCRhTTHnPdNGsTJBw+IuiQREZG9ysnO4uaPHc7HjxpBzOG6v83nr3PWRF3WPikIiySZpxZu5sV3y8nLzuK75xwSdTkiIiIdkp1l/PQjU7j0uFG4wy+eXkZ1fVPUZe2VtqcSSSJ1jc386NGFAFx98hhG9e8dcUUiIiIdl5Vl3HDeofQvyuNDU4cm/U6oyV2dSIa5Y9YK1m6vZVBJPp895aCoyxEREek0M+PLZxwcdRkdoiCcZP7n8cVkGVx7yjiKC3KjLkd60MaKWn7z3HIArv/AIUn/U7SIiEiq0/+0SWTdjhrumLWCppjzt9fX8pX3HczHjxpBjlYMyAg/fWwxtY3NHDWqL+dOHRp1OSIiImlPCSuJDOvTi99efARjynpTvquB7zz4Nh/41SxmLt0adWnSzeas2s7D8zdgBt8/91DMLOqSRERE0p6CcBIxM848dDBPfvlkvvehSfQpzGXp5l1c9sfZfPKPs1myqSrqEqUbNMec7z30DgAXHj2CycNKI65IREQkMygIJ6G8nCyuOGEMM792KledOIbcbOOFpVs5+5cv8O1/LmBrVX3UJUoX+uuctSzcWElxQQ5fO3NC1OWIiIhkDAXhJFZamMt3PziJp6+bwdmTBxNzuG/2Gk658Tl+89y71DU2R12iHKCKmkZufHIxAF8542D6F+VHXJGIiEjmUBBOAaP69+Z3lxzJ3z8znanDS6luaObGJ5dw2k3P86+564nFPOoSZT/9/Oml7KhpZPzAIi6dPirqckRERDKKuStE7Y2ZlQAVFRUVlJSURF0OsZjzyFsb+NkTS1i/sxaAqcNL+c45kzhmTL+Iq5POWLKpig/8ahbNMefuK4/hpPHaSllERGR/VFZWUlpaClDq7pUdvZ9mhFNMVpZx3uHDeOarM/j6WRMoys9h/roKPnbbK3zm7jdYVV4ddYnSAe7ODf9+h+aYc+akQQrBIiIiEdCM8D4k24xwa1ur6vn500u5f/YaYg652cYnp4/mi6eNp7RQG3Ikm7Xba3j87Y08umAT89fuJC8ni6e/MoOR/QujLk1ERCRl7e+MsILwPiR7EI5burmKnzy2iOeXBGsO9ynM5YunjeeS40aRl6OJ/yitKq/m8bc38diCjSxYX9Fy3Az++4OTuOKEMRFWJyIikvoUhLtJqgThuBeWbuXHjy5iyeZgzeExZb351tkTOXPSIG3S0INWbN3FYws28tiCTSzcuPv9mGVw3Nj+nD1lCGcdOoiBxQURVikiIpIeFIS7SaoFYQg2aPjb62u5+amllO8K1hw+dkw/vnvOJKYM12YN3eXdLVU8+tYmHn97I4sTNj/JzjKOH9efsycP4cxDB1GmJdJERES6lIJwN0nFIBy3q76J22Yu5/YXVlDfFAPgI9OG8fX3T2BIaa+Iq0t97s7Szbt4dMFGHl+wkWVbdrWM5WQZJxxUxgemDObMSYPp2zsvwkpFRETSm4JwN0nlIBy3YWctNz25hH/OXQ9AQW4WR47qy9iyIsYO6M3YAUWMLevNsD69yMpS+8TeuDsLN1by+IJNPPb2RlZs3b1KR262cdL4AZw9OQi/ulhRRESkZygId5N0CMJxb63byY8eXcTsldvbHM/PyWJMWW/GDYgH5N4tYbm4IHNDnbvz9vpKHns7mPldta2mZSwvJ4uTxw/gnMMGc9rEQZT2ytyvk4iISFQUhLtJOgVh2B3qlmyuYsXWXazYWs2K8l2sKq+hoTnW7v0GFOcztiyYPR4XhuRxA4oY3reQ7DScRW6OOQvWV/D4go089vZG1m6vbRnLz8ni1AkDOXvKYE6bODCjf0gQERFJBgrC3STdgnB7mmPOuh01rNhazfKtu1hRXs2KrbtYvrWarVX17d4vLzuLUf0L39NiMXZAEYNKggvC3MMbHv4ZhHEPxwiPxxLPSfi45THw4JyE+zY0xahraqa+MUZ9+Gf887rGZuqbdv9Z39RMXWNbf8aob3VuXWMzTa22re6Vm81pE4Pwe+qEgfTOz+mOb4OIiIjsBwXhbpIpQXhvKusaWRnOHK/YWt0SlleWV7dchJeOCvOyOf2QQXxg8mBOmTCQXnnZUZckIiIibdjfIJwx01pmNhm4EzgIuAP4huungA4pKchl6og+TB3R5z3HYzFn/c7altnjFQlheVt1A1kGhmEGBpgZBpD4uUFWeNzCwd3nt3F/2308PyeL/Nws8nOyKWjnz/h4fk4WBbl7/zM/N4uC8D4FOdkUFeSQm63NSERERNJVRswIm1k+sBh4ErgR+BXwgLvf2YH7ZvyMsIiIiEgy298Z4UyZ7jobKAWuc/flwPXAldGWJCIiIiJRypTWiKnAq+4eX/fqLWBSWyeGs8eJW38Vd3NtIiIiIhKBTJkRLgFWxj8Je4ObzaxvG+d+G6hIuK3rkQpFREREpEdlShBuAlqvAVYHFLZx7k8J2ijit+HdW5qIiIiIRCFTWiO2A5NbHSsGGlqf6O71JIRms/TbLEJEREREMmdGeA4wPf6JmY0h6ANue69hEREREUl7mRKEXwBKzOyK8PPrgafdvTnCmkREREQkQhnRGuHuTWZ2FXCfmd0IxIBToq1KRERERKKUEUEYwN0fNrNxwJEES6lti7omEREREYlOxgRhAHffBDwadR0iIiIiEr1M6REWEREREXkPBWERERERyUgKwiIiIiKSkRSERURERCQjKQiLiIiISEZSEBYRERGRjKQgLCIiIiIZSUFYRERERDJSRm2ocSAqKyujLkFERERE2rC/Oc3cvYtLSS9mNgxYF3UdIiIiIrJPw919fUdPVhDeBzMzYChQFR4qJgjGwxOOSfrR9zlz6HudGfR9zhz6XmeGtr7PxcAG70S4VWvEPoRfzJafLIJcDECVu6tfIk3p+5w59L3ODPo+Zw59rzNDO9/nTn+/dbGciIiIiGQkBWERERERyUgKwp1XD/wg/FPSl77PmUPf68yg73Pm0Pc6M3TJ91kXy4mIiIhIRtKMsIiIiIhkJAVhEREREclICsIirZjZr8zME27vRl2TiHSOmZWZ2UozG51wTO9tkRRlZueZ2QozazKzeWZ2SHj8gN7XCsKdYGaTzWyOme0wsxstYRE7SStHAecAfcPbtGjLka7UTkDSezuNmFkZ8G9gdKshvbfTzF7Ckd7TacTMxgF3At8ChgFLgTvC4QN6XysId5CZ5QOPAG8QfNEnAZdHWZN0PTPLAQ4FXnD3neFNOxOlibYCkt7bael+4N7EA3pvp5/2wpHe02npEOBb7v43d98M/A6Y1hXvawXhjjsbKAWuc/flwPXAldGWJN1gCsH7Yp6Z1ZrZE2Y2MuqipMvsEZDQezsdXe3uv2p1TO/t9NNmOELv6bTj7v9299sTDk0AltEF72sF4Y6bCrzq7jXh528R/JQp6WUSsAS4FDgMaAJu3+s9JJW0FZD03k4z7r6yjcN6b6eZvYQjvafTmJnlAV8FbqUL3tdaR7iDzOxmoMDdP5dwbCtwsLvviK4y6U7hT5Yrgb7asz59mJkDY9x9ld7b6Svx+9zGmN7baSQMR+8A/wcchN7TacvMfkow63+0uze2Guv0+1ozwh3XxJ67l9QBhRHUIj1nC8H7ZEjUhUi30Xs7M+m9nV5+AFQTXECl93SaMrPTgM8BF7UOwaFOv68VhDtuOzCg1bFioCGCWqSbhFcXX5RwaDoQA9ZGVJJ0P723M4De2+mrjXCk93QaMrMxwH3A59x9YXjsgN/XOV1aZXqbA1wd/yT8huQTvOEkfcwHfmRmm4Fs4NfAnxN6zST96L2dGfTeTkNthSP0nk47ZtaLYMWfh4AHzawoHHqLA3xfa0a4414ASszsivDz64Gn3b05wpqki7n7X4C/Av8g+Mf1CeDzkRYl3U3v7Qyg93b6aSschQFpFnpPp5szCS6MuxqoSrjN4gDf17pYrhPM7FyCL3QtwdT7KQk/gYpIimh9EZXe2yKpx8zOA/7VxtAYghUE9J6WfVIQ7iQzGwwcSbA0y7ao6xGRrqH3tkh60XtaOkJBWEREREQyknqERURERCQjKQiLiIiISEZSEBYRERGRjKQgLCKS5szsS2b2tfDjHDPLN7MO//tvZmM6c76ISKrQP2wiImnCzH5gZg+0MTQX+LGZHQ1cBFQAFWa2M+HWaGafT3isE8zsZjObRbAZxalmNtrMmsxslZltNLPZPfLCRES6iYKwiEgKMLNmM6tMCK1faOO0BtrYRtbdXwB+C5S4+5/dvcDdi929T/wGPAnUJdztZGA88Bmgn7s/Ex5f7+6jgfMJ1mgVEUlZ2mJZRCQ1NAJHuPu7ZvZvoMnMvh8ejwEOnAAMNrP/Bzzk7m+Z2XKC7We/0oHnaEr4uA5Y4+7vAJiZAf2BrQnnaP1NEUlpCsIiIqmhqdXnjcDC8HgTQSgdD+QD7xC0P0AwQ1wPYGZvASWtHuc2d/9pG8/nwCfN7HygD8EWp58EXg7HdwHTzOwmYLa7/22/X5mISEQUhEVEUkMMeNPMYkBv4B/u/jczO9zd5wGY2QQg293/mXC/ZnbP3A4GjknYWvp/gF57ec4/u/vnw3MLgP8HXA3g7m+b2TXAEGBF17xEEZGepSAsIpIaYsBRCa0RbmYHA6+a2SR370gYjXXwWItwtYg8ghnoPwPTzezYcLgZeNHdX+/wqxARSSK6WE5EJDXs8e+1uy8FHiCYqe2oF8NVH1YB1+7j3CuBbUA5cCxwHvBz4Ljw9jPgxE48t4hIUlEQFhFJDdmtPrfwz9vp3OoNJ7r76HDlh9/t49w/uHtfdy9y95cJeoRzgJ8C3yPoHW5ruTYRkZSg1ggRkdSQDcwOe4RLgIegZWm0Fzr4GNbGsfYmRAwYaGYXAtOAme7+mJn9E/gCUA087u7rOvEaRESSioKwiEhqyCG40C3eI5y7H4+xGXg+WAmtxW2tTzKznwFfAdYA64AFwLxw+Cfhx83AUftRg4hI0lAQFhFJDVvZvYTa1UBNG+eUsOfavoWE/9a7+2FtPXC4RnBZwmP+ENgEjHX368Jzcs3sNOBzwCpgIPC/ZvYg8Iq7r92/lyUiEh0FYRGRFODuQxI+3pg4ZmZFwKvAwQQ7wSUqAgrae1wzKwOWEcwWvxg+fpWZ5cXvZ2b5wGsEAfj37v5o+JyfBr5OsHzaxw/g5YmIRMLctTGQiEiqM7NJwBZ3L9+P+w519w37OMe8nf8w9jYmIpLMFIRFREREJCNp+TQRERERyUgKwiIiIiKSkRSERURERCQjKQiLiIiISEZSEBYRERGRjKQgLCIiIiIZSUFYRERERDKSgrCIiIiIZCQFYRERERHJSArCIiIiIpKR/j8eutpiZ7k8YgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 依据“step”列进行分组，再统计每组的数据量（即每个时间段的交易笔数）\n",
    "series_1 = data['step'].value_counts(sort=False) # sort默认为TRUE，会对结果进行排序，False就是不进行排序\n",
    "\n",
    "# 设置画布的大小和分辨率\n",
    "plt.figure(figsize=(8,6), dpi=100)\n",
    "\n",
    "# 绘制折线图\n",
    "plt.plot(series_1)\n",
    "\n",
    "# 设置标题\n",
    "plt.title('金融交易时间分布状况',fontsize=13)\n",
    "\n",
    "# 设置横坐标\n",
    "plt.xlabel('时间段')\n",
    "\n",
    "# 设置纵坐标\n",
    "plt.ylabel('交易笔数')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9c091003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '交易笔数')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 依据时间段,对isFraud列进行分组求和\n",
    "series_2=data.groupby(data['step'])['isFraud'].sum() \n",
    "\n",
    "# 设置画布的大小和分辨率\n",
    "plt.figure(figsize=(8,6), dpi=100)\n",
    "\n",
    "# 绘制折线图\n",
    "sns.lineplot(data=series_2)\n",
    "\n",
    "# 设置标题\n",
    "plt.title('金融诈骗交易时间分布状况',fontsize=13)\n",
    "\n",
    "# 设置横坐标\n",
    "plt.xlabel('时间段')\n",
    "\n",
    "# 设置纵坐标\n",
    "plt.ylabel('交易笔数')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "343540c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '金融交易诈骗率')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建新的数据框，列名是'rate_zhapian'，各时段诈骗率=各时段金融诈骗交易笔数/各时段金融交易总笔数\n",
    "df=pd.DataFrame(columns=['rate_zhapian'])\n",
    "df['rate_zhapian']=series_2/series_1\n",
    "\n",
    "# 设置画布的大小和分辨率\n",
    "plt.figure(figsize=(8,6), dpi=100)\n",
    "\n",
    "# 画折线图，可视化一天各个时段的金融诈骗发生率\n",
    "plt.plot(df['rate_zhapian'])\n",
    "\n",
    "# 设置标题\n",
    "plt.title('各时段金融交易诈骗率',fontsize=13)\n",
    "\n",
    "# 设置横坐标\n",
    "plt.xlabel('时间段')\n",
    "\n",
    "# 设置纵坐标\n",
    "plt.ylabel('金融交易诈骗率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73d0c03d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '交易数量')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看金融交易各个类型的交易总数：依据type进行分组，然后计算各个type的数量\n",
    "data.groupby(by=['type'])['step'].count()\n",
    "# 绘制出金融交易类别分布条形图\n",
    "\n",
    "# 设置画布的大小和分辨率\n",
    "plt.figure(figsize=(8,6), dpi=100)\n",
    "\n",
    "# 绘制条形图，并设置标题\n",
    "data.groupby(by=['type'])['step'].count().plot(kind='bar', rot=360, title='交易类别数量分布条形图') # rot: 轴标签的旋转度数\n",
    "\n",
    "# 设置横坐标\n",
    "plt.xlabel('交易类型')\n",
    "\n",
    "# 设置纵坐标\n",
    "plt.ylabel('交易数量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "365229fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "CASH_IN       0\n",
      "CASH_OUT    141\n",
      "DEBIT         0\n",
      "PAYMENT       0\n",
      "TRANSFER    130\n",
      "Name: isFraud, dtype: int64\n",
      "金融诈骗交易 271\n",
      "金融诈骗交易余额清空 267\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '交易数量')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 依据'type'进行分组，然后计算各个交易类别的金融诈骗交易事件（isFraud=1）的总数\n",
    "fraudsum=data.groupby(by=['type'])['isFraud'].sum()\n",
    "print(fraudsum)\n",
    "print('金融诈骗交易',fraudsum.sum())\n",
    "# 筛选出金融诈骗交易\n",
    "df_zhapian=data[data[\"isFraud\"]==1]  \n",
    "\n",
    "# 查看所有金融诈骗交易中，交易后原账户余额为0的账号数量：\n",
    "fraud0sum=df_zhapian[df_zhapian[\"newbalanceOrig\"]==0][\"isFraud\"].count() \n",
    "print('金融诈骗交易余额清空',fraud0sum)\n",
    "# 绘制出金融交易类别分布条形图\n",
    "\n",
    "# 设置画布的大小和分辨率\n",
    "plt.figure(figsize=(8,6), dpi=100)\n",
    "\n",
    "# 绘制条形图，并设置标题\n",
    "fraudsum.plot(kind='bar', rot=360, title='金融诈骗交易类别数量分布条形图') # rot: 轴标签的旋转度数\n",
    "\n",
    "# 设置横坐标\n",
    "plt.xlabel('交易类型')\n",
    "\n",
    "# 设置纵坐标\n",
    "plt.ylabel('交易数量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfa26778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "金融诈骗交易的平均金额 779202.3153505534\n",
      "非金融诈骗交易的平均金额 272962.33735056536\n"
     ]
    }
   ],
   "source": [
    "# 计算金融诈骗交易的平均金额\n",
    "fraudmean=data[data[\"isFraud\"]==1]['amount'].mean()\n",
    "print('金融诈骗交易的平均金额',fraudmean)\n",
    "# 计算非金融诈骗交易时，涉及现金流出CASH_OUT和转账TRANSFER的正常金融交易业务的平均金额\n",
    "fraud0mean=data[(data[\"isFraud\"]==0)&(data[\"type\"].isin([\"CASH_OUT\",\"TRANSFER\"]))]['amount'].mean()\n",
    "print('非金融诈骗交易的平均金额',fraud0mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95290c82",
   "metadata": {},
   "source": [
    "## 对身份信息进行探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f79345c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1   PAYMENT   9839.64       170136.0       160296.36             0.0   \n",
       "1     1   PAYMENT   1864.28        21249.0        19384.72             0.0   \n",
       "2     1  TRANSFER    181.00          181.0            0.00             0.0   \n",
       "3     1  CASH_OUT    181.00          181.0            0.00         21182.0   \n",
       "4     1   PAYMENT  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  \n",
       "0             0.0        0  \n",
       "1             0.0        0  \n",
       "2             0.0        1  \n",
       "3             0.0        1  \n",
       "4             0.0        0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将'nameOrig','nameDest'这两列与分类问题无关的列删除，得到用于分类的新数据集\n",
    "data_for_classification = data.drop(['nameOrig','nameDest'], axis=1)\n",
    "data_for_classification.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35d4e5b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1     3   9839.64       170136.0       160296.36             0.0   \n",
       "1     1     3   1864.28        21249.0        19384.72             0.0   \n",
       "2     1     4    181.00          181.0            0.00             0.0   \n",
       "3     1     1    181.00          181.0            0.00         21182.0   \n",
       "4     1     3  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  \n",
       "0             0.0        0  \n",
       "1             0.0        0  \n",
       "2             0.0        1  \n",
       "3             0.0        1  \n",
       "4             0.0        0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从LabelEncoder类中，调用函数fit_transform，对分类型特征type进行编码，映射到0~4\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "type_encoder = LabelEncoder() \n",
    "data_for_classification['type'] = type_encoder.fit_transform(data_for_classification['type'])\n",
    "\n",
    "data_for_classification.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "51f62812",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "x = data_for_classification.drop(['isFraud'], axis=1)\n",
    "y = data_for_classification['isFraud']\n",
    "\n",
    "# 函数最终将返回四个变量，分别为`x`的训练集和测试集，以及`y`的训练集和测试集。\n",
    "x_train,x_test,y_train,y_test = model_selection.train_test_split(x, y, \n",
    "                                         random_state=2022, # 随机种子\n",
    "                                         test_size=0.2, # 测试集比例\n",
    "                                         stratify=data_for_classification[\"isFraud\"]) #保持比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73d8373d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators = 20,                # 基学习器个数\n",
    "                            random_state=2022,                   # 随机种子\n",
    "                            #class_weight={0:1, 1:1000})\n",
    "                            class_weight='balanced' )          # 类别权重\n",
    "rf.fit(x_train, y_train)\n",
    "y_pred_rf = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67a715d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[114793      4]\n",
      " [    25     29]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    114797\n",
      "           1       0.88      0.54      0.67        54\n",
      "\n",
      "    accuracy                           1.00    114851\n",
      "   macro avg       0.94      0.77      0.83    114851\n",
      "weighted avg       1.00      1.00      1.00    114851\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 输出混淆矩阵\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred_rf)\n",
    "print(confusion_matrix)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "\n",
    "# 创建总画布窗口\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "# 绘制热力图，设置图像参数\n",
    "# annot=True:热力图的每个单元上显示数值；annot_kws:设置单元格中数值标签的其他属性；\n",
    "# fmt：指定单元格中数据的显示格式；cmap：用于热力图的填充色，'YlGnBu_r'代表数字越大，颜色越浅\n",
    "sns.heatmap(confusion_matrix,annot=True,annot_kws={'size':15}, fmt='d',cmap = 'YlGnBu_r')\n",
    "\n",
    "# 设置横纵坐标与标题\n",
    "plt.ylabel('真实值')\n",
    "plt.xlabel('预测值')\n",
    "plt.title('随机森林混淆矩阵热力图')\n",
    "print(classification_report(y_test, y_pred_rf))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "935650a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建XGBoost模型\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "xgbt = XGBClassifier(n_estimators=100,                 # 基学习器\n",
    "                          learning_rate=0.3,           # 学习率\n",
    "                          random_state=5,              # 随机种子\n",
    "                          objective='binary:logistic', # 目标函数\n",
    "                          scale_pos_weight=100)        # 正样本权重 \n",
    "xgbt.fit(x_train, y_train)\n",
    "y_pred_xgbt = xgbt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5cbf614b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[114787     10]\n",
      " [    12     42]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    114797\n",
      "           1       0.81      0.78      0.79        54\n",
      "\n",
      "    accuracy                           1.00    114851\n",
      "   macro avg       0.90      0.89      0.90    114851\n",
      "weighted avg       1.00      1.00      1.00    114851\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 输出混淆矩阵\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred_xgbt)\n",
    "print(confusion_matrix)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "\n",
    "# 创建总画布窗口\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "# 绘制热力图，设置图像参数\n",
    "# annot=True:热力图的每个单元上显示数值；annot_kws:设置单元格中数值标签的其他属性；\n",
    "# fmt：指定单元格中数据的显示格式；cmap：于热力图的填充色，'YlGnBu_r'代表数字越大，颜色越浅\n",
    "sns.heatmap(confusion_matrix,annot=True,annot_kws={'size':15}, fmt='d',cmap = 'YlGnBu_r')\n",
    "\n",
    "# 设置横纵坐标与标题\n",
    "plt.ylabel('真实值')\n",
    "plt.xlabel('预测值')\n",
    "plt.title('XGBoost混淆矩阵热力图')\n",
    "print(classification_report(y_test, y_pred_xgbt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a43db664",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过网格搜索选择最优参数\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = [{\n",
    "    'n_estimators':[50,100,150,200,300],\n",
    "    'scale_pos_weight':[50,100,200,500,1000,2000]\n",
    "}]\n",
    "grid_search = GridSearchCV(xgbt, param_grid, scoring = 'f1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c82da946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输出最佳参数组合以及分数\n",
    "grid_search.fit(x, y)\n",
    "\n",
    "print(\"best params:\", grid_search.best_params_)\n",
    "\n",
    "print(\"best score:\", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73bf2e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输出应用最佳参数组合时，模型的评价指标结果\n",
    "from sklearn.metrics import recall_score, precision_score,f1_score\n",
    "\n",
    "best_estimator = grid_search.best_estimator_\n",
    "\n",
    "print(\"test precision：{:.3f}\".format(precision_score(y_test, best_estimator.predict(x_test))))\n",
    "print(\"test recall：{:.3f}\".format(recall_score(y_test, best_estimator.predict(x_test))))\n",
    "print(\"test f1-score：{:.3f}\".format(f1_score(y_test, best_estimator.predict(x_test))))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
